This application is based upon and claims the benefit of priority from prior Japanese Patent Application P2001-263548 filed on Aug. 31, 2001; the entire contents of which are incorporated herein by reference.
1. Field of the Invention
The present invention relates to a diagnostic method, which predicts life of manufacturing equipment that uses a rotary machine. In particular it is related to a diagnostic method, which predicts the life of a dry pump used in semiconductor manufacturing equipment, such as low-pressure chemical vapor deposition (LPCVD) equipment or dry etching equipment.
2. Description of the Related Art
Failure diagnosis has become important to ensure efficient semiconductor device manufacturing. In recent years, especially as the trend towards many item/small volume production of system LSI grows, an efficient yet highly adaptable semiconductor device manufacturing method has become necessary. It is possible to use a small-scale production line for efficient production of semiconductors. However, if the large-scale production line is merely shortened, investment efficiency may be reduced because of problems such as a drop in manufacturing equipment capacity utilization. To rectify this situation, there is a method where a plurality of manufacturing processes are performed by one piece of manufacturing equipment. For example, in LPCVD equipment using a dry pump for the evacuation system, reactive gases and reaction products differ and formation situations for the reaction products within the dry pump differ depending on the type of manufacturing processes. Therefore, the manufacturing process affects the life of the dry pump.
If the dry pump should have a shutdown during a specific manufacturing process, then the lot being processed becomes defective. Moreover, excessive maintenance of the manufacturing equipment may become necessary due to microscopic dust caused by residual reactive gases within the manufacturing equipment. Implementation of such excessive maintenance causes the manufacturing efficiency of the semiconductor device to drop dramatically. If regular maintenance is scheduled with a margin of safety in order to prevent such sudden shutdowns during the manufacturing process, the frequency of maintenance work on the dry pump may become astronomical. Not only does this increase maintenance costs, but also the decrease in capacity utilization of the semiconductor manufacturing equipment becomes remarkable due to changing the dry pump, causing the manufacturing efficiency of the semiconductor device to sharply decline. In order to use of semiconductor manufacturing equipment in common for a plurality of processes, as is necessary for an efficient small-scale production line, it is desirable to accurately diagnose vacuum pump life and to operate the dry pump without having any waste in terms of time.
Previously, some methods of diagnosing dry pump life have been proposed. For example, a method using the Mahalanobis distance (MD) is a highly sensitive diagnosis of failure occurrences. With the method of diagnosing life according to the MD, data for characteristics in which homogeneity may be promised is gathered to form the recognition space by only utilizing data from the same conditions. In other words, when characteristics are measured under normal conditions, the characteristics are expected to be relatively homogeneous. In the method of diagnosing life according to the MD, such normal condition characteristics, in which homogeneity may be promised, are gathered to form the xe2x80x9cMahalanobis space (reference space)xe2x80x9d, which is a space for determining life. Since a set of data of characteristics for normal conditions forms a space to become a reference for measurement and has a certain correlation, the Mahalanobis space is represented by the inverse matrix of a correlation matrix derived from the set of characteristics data. The MD is a measure indicating the degree of abnormality in the characteristics data to be measured, that is, it indicates how far the measured characteristics data deviate from the characteristics data under normal conditions, and this is forms the reference for measurement. The MD takes a value between zero and infinity. If the value is small, it may be determined as a group of normal data; whereas if the value is large, the probability of abnormality is high, and therefore it is determined that life may be short.
The key to a life diagnosis method based on the MD lies in determining the characteristics and forming the Mahalanobis space (reference space) representing normal conditions. With the conventionally proposed method of diagnosing life according to the MD, a reference space is formed by utilizing data only immediately after doing maintenance. Therefore, the effects of variation during the semiconductor manufacturing process, such as pressure variation or variation of the amount of gas flow, and power supply may not be eliminated, and thus it is difficult to diagnose with accuracy.
In addition, with the dry pump, there is a problem of disparity between equipment where characteristics such as motor current vary according to device. Such disparity between equipment has been an obstacle to highly accurate diagnosis.
As described above, with the conventional dry pump failure diagnosis method, there are problems such as the fact that highly accurate diagnosis is difficult because of effects such as variation in process conditions, power supply variation, and the disparity between equipment relating to semiconductor manufacturing.
According to a first aspect of the present invention, a method for diagnosing life of manufacturing equipment having a rotary machine, includes: measuring reference time series data representing characteristics in a state before deterioration of the manufacturing equipment occurs; finding a reference auto covariance function based on the reference time series data; extracting a reference variation caused by variations of the process condition and power supply from the reference auto covariance function, and calculating a cycle of the reference variation; measuring diagnostic time series data representing the characteristics in a sequence to be measured of the manufacturing equipment; finding a diagnostic auto covariance function based on the diagnostic time series data; and determining the life of the manufacturing equipment from the diagnostic auto covariance function using a component with a cycle shorter than a cycle of the reference variation.
According to a second aspect of the present invention, a method for diagnosing life of manufacturing equipment having a rotary machine, includes: measuring reference time series data before starting measurement of diagnostic time series data for characteristics of the manufacturing equipment; setting a Mahalanobis space from the reference time series data; measuring the diagnostic time series data; calculating a time variation of a Mahalanobis distance of the diagnostic time series data by using the diagnostic time series data and the Mahalanobis space; setting a new Mahalanobis space from the diagnostic time series data when the Mahalanobis distance reaches a threshold value; and determining the life of the manufacturing equipment by comparing a new Mahalanobis distance corresponding to the new Mahalanobis space with the threshold value.